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Review
. 2023 Dec;44(Suppl 3):222-224.
doi: 10.1007/s00292-023-01262-w. Epub 2023 Nov 21.

Digital image analysis and artificial intelligence in pathology diagnostics-the Swiss view

Sabina Berezowska  1 , Gieri Cathomas  2 , Rainer Grobholz  3   4 , Maurice Henkel  5   6   7 , Wolfram Jochum  8 , Viktor H Koelzer  9 , Mario Kreutzfeldt  10   11 , Kirsten D Mertz  12 , Matthias Rössle  13 , Davide Soldini  14 , Inti Zlobec  2 , Andrew Janowczyk  15   16   17
Affiliations
Review

Digital image analysis and artificial intelligence in pathology diagnostics-the Swiss view

Sabina Berezowska et al. Pathologie (Heidelb). 2023 Dec.

Abstract
in English, German

Digital pathology (DP) is increasingly entering routine clinical pathology diagnostics. As digitization of the routine caseload advances, implementation of digital image analysis algorithms and artificial intelligence tools becomes not only attainable, but also desirable in daily sign out. The Swiss Digital Pathology Consortium (SDiPath) has initiated a Delphi process to generate best-practice recommendations for various phases of the process of digitization in pathology for the local Swiss environment, encompassing the following four topics: i) scanners, quality assurance, and validation of scans; ii) integration of scanners and systems into the pathology laboratory information system; iii) the digital workflow; and iv) digital image analysis (DIA)/artificial intelligence (AI). The current article focuses on the DIA-/AI-related recommendations generated and agreed upon by the working group and further verified by the Delphi process among the members of SDiPath. Importantly, they include the view and the currently perceived needs of practicing pathologists from multiple academic and cantonal hospitals as well as private practices.

Digitale Pathologe (DP) wird zunehmend in der Routinediagnostik der klinischen Pathologie eingesetzt. Mit fortschreitender Digitalisierung der Routinefälle wird die Implementierung digitaler Bildanalysealgorithmen und künstlicher Intelligenz nicht nur machbar, sondern auch für die tägliche Praxis wünschenswert. Das Schweizer Konsortium für Digitale Pathologie (Swiss Digital Pathology Consortium, SDiPath) hat einen Delphi-Prozess initiiert, um Empfehlungen für das beste Vorgehen in Bezug auf verschiedene Phasen des Digitalisierungsprozesses in der Pathologie für die lokalen Verhältnisse in der Schweiz zu erstellen, dazu gehören die folgenden 4 Themen: i) Scanner, Qualitätssicherung und Validierung der eingescannten Schnittpräparate; ii) Integration von Scannern und Systemen in das Laborinformationssystem; iii) der digitale Arbeitsablauf; und iv) digitale Bildanalyse (DIA)/künstliche Intelligenz (KI). Im vorliegenden Beitrag liegt der Schwerpunkt auf den Empfehlungen hinsichtlich DIA und KI, die von einer Arbeitsgruppe erarbeitet und mittels Delphi-Prozess durch die Mitglieder des SDiPath-Konsortiums bestätigt wurden. Wichtig ist, dass sie die Ansichten und die aktuellen Bedürfnisse praktisch tätiger Pathologen aus verschiedenen Lehr- und Kantonsspitälern sowie aus privaten Praxen einbeziehen.

Keywords: Artificial intelligence; Delphi process; Digitalization; Image analysis; Pathology.

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Conflict of interest statement

V.H. Koelzer reports being an invited speaker for Sharing Progress in Cancer Care (SPCC) and Indica Labs; advisory board of Takeda; sponsored research agreements with Roche and IAG, all unrelated to the current study. S. Berezowska, G. Cathomas, R. Grobholz, M. Henkel, W. Jochum, M. Kreutzfeldt, K.D. Mertz, M. Rössle, D. Soldini, I. Zlobec and A. Janowczyk declare that they have no competing interests.

References

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